SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2001–2050 of 3304 papers

TitleStatusHype
Segmenting thalamic nuclei from manifold projections of multi-contrast MRI—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Self-calibrating Neural Networks for Dimensionality Reduction—0
Self-Expressive Decompositions for Matrix Approximation and Clustering—0
Self-paced Principal Component Analysis—0
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers—0
Self-Supervised Graph Embedding Clustering—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning—0
SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning—0
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining—0
Semi-supervised deep learning for high-dimensional uncertainty quantification—0
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection—0
Semi-supervised Deep Representation Learning for Multi-View Problems—0
Semi-supervised Fisher vector network—0
Semi-supervised Learning based on Distributionally Robust Optimization—0
Semi-supervised Learning with Explicit Relationship Regularization—0
Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings—0
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction—0
Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits—0
Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization—0
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity—0
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives—0
Sequential Dimensionality Reduction for Extracting Localized Features—0
Sequential Labeling with online Deep Learning—0
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly—0
Sequential Low-Rank Change Detection—0
Sequential Principal Curves Analysis—0
SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network—0
Shamap: Shape-based Manifold Learning—0
SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion—0
Shape-informed surrogate models based on signed distance function domain encoding—0
Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence—0
ShapeVis: High-dimensional Data Visualization at Scale—0
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities—0
Shining light on data: Geometric data analysis through quantum dynamics—0
SHOE: Supervised Hashing with Output Embeddings—0
Siamese networks for Poincaré embeddings and the reconstruction of evolutionary trees—0
Siamese Neural Networks for Wireless Positioning and Channel Charting—0
Signed graphs in data sciences via communicability geometry—0
Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales—0
Similarity Search with Tensor Core Units—0
Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions—0
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images—0
Simple strategies for recovering inner products from coarsely quantized random projections—0
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
Simple, unified analysis of Johnson-Lindenstrauss with applications—0
Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning—0
Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified